[Mlir-commits] [mlir] [mlir][linalg] Add TransposeConv2D Transform Op (PR #68567)
Nicolas Vasilache
llvmlistbot at llvm.org
Thu Nov 23 08:57:12 PST 2023
================
@@ -0,0 +1,254 @@
+// RUN: mlir-opt %s -test-transform-dialect-interpreter -split-input-file -verify-diagnostics | FileCheck %s
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_f64
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xf64>, %[[FILTER:.+]]: tensor<8x2x2x6xf64>, %[[INIT:.+]]: tensor<1x2x2x8xf64>) -> tensor<1x2x2x8xf64> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xf64>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xf64>) outs(%[[NEWF]] : tensor<2x2x6x8xf64>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xf64>, tensor<2x2x6x8xf64>) outs(%[[INIT]] : tensor<1x2x2x8xf64>) -> tensor<1x2x2x8xf64>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xf64>
+func.func @conv_2d_nhwc_fhwc_f64(%input: tensor<1x4x4x6xf64>, %filter: tensor<8x2x2x6xf64>, %init: tensor<1x2x2x8xf64>) -> tensor<1x2x2x8xf64> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xf64>, tensor<8x2x2x6xf64>)
+ outs (%init: tensor<1x2x2x8xf64>) -> tensor<1x2x2x8xf64>
+ return %0 : tensor<1x2x2x8xf64>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_f32
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xf32>, %[[FILTER:.+]]: tensor<8x2x2x6xf32>, %[[INIT:.+]]: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xf32>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xf32>) outs(%[[NEWF]] : tensor<2x2x6x8xf32>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xf32>, tensor<2x2x6x8xf32>) outs(%[[INIT]] : tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xf32>
+func.func @conv_2d_nhwc_fhwc_f32(%input: tensor<1x4x4x6xf32>, %filter: tensor<8x2x2x6xf32>, %init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xf32>, tensor<8x2x2x6xf32>)
+ outs (%init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
+ return %0 : tensor<1x2x2x8xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_f16
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xf16>, %[[FILTER:.+]]: tensor<8x2x2x6xf16>, %[[INIT:.+]]: tensor<1x2x2x8xf16>) -> tensor<1x2x2x8xf16> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xf16>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xf16>) outs(%[[NEWF]] : tensor<2x2x6x8xf16>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xf16>, tensor<2x2x6x8xf16>) outs(%[[INIT]] : tensor<1x2x2x8xf16>) -> tensor<1x2x2x8xf16>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xf16>
+func.func @conv_2d_nhwc_fhwc_f16(%input: tensor<1x4x4x6xf16>, %filter: tensor<8x2x2x6xf16>, %init: tensor<1x2x2x8xf16>) -> tensor<1x2x2x8xf16> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xf16>, tensor<8x2x2x6xf16>)
+ outs (%init: tensor<1x2x2x8xf16>) -> tensor<1x2x2x8xf16>
+ return %0 : tensor<1x2x2x8xf16>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_b16
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xbf16>, %[[FILTER:.+]]: tensor<8x2x2x6xbf16>, %[[INIT:.+]]: tensor<1x2x2x8xbf16>) -> tensor<1x2x2x8xbf16> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xbf16>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xbf16>) outs(%[[NEWF]] : tensor<2x2x6x8xbf16>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xbf16>, tensor<2x2x6x8xbf16>) outs(%[[INIT]] : tensor<1x2x2x8xbf16>) -> tensor<1x2x2x8xbf16>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xbf16>
+func.func @conv_2d_nhwc_fhwc_b16(%input: tensor<1x4x4x6xbf16>, %filter: tensor<8x2x2x6xbf16>, %init: tensor<1x2x2x8xbf16>) -> tensor<1x2x2x8xbf16> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xbf16>, tensor<8x2x2x6xbf16>)
+ outs (%init: tensor<1x2x2x8xbf16>) -> tensor<1x2x2x8xbf16>
+ return %0 : tensor<1x2x2x8xbf16>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xi64>, %[[FILTER:.+]]: tensor<8x2x2x6xi64>, %[[INIT:.+]]: tensor<1x2x2x8xi64>) -> tensor<1x2x2x8xi64> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xi64>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xi64>) outs(%[[NEWF]] : tensor<2x2x6x8xi64>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xi64>, tensor<2x2x6x8xi64>) outs(%[[INIT]] : tensor<1x2x2x8xi64>) -> tensor<1x2x2x8xi64>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xi64>
+func.func @conv_2d_nhwc_fhwc_i64(%input: tensor<1x4x4x6xi64>, %filter: tensor<8x2x2x6xi64>, %init: tensor<1x2x2x8xi64>) -> tensor<1x2x2x8xi64> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xi64>, tensor<8x2x2x6xi64>)
+ outs (%init: tensor<1x2x2x8xi64>) -> tensor<1x2x2x8xi64>
+ return %0 : tensor<1x2x2x8xi64>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_i32
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xi32>, %[[FILTER:.+]]: tensor<8x2x2x6xi32>, %[[INIT:.+]]: tensor<1x2x2x8xi32>) -> tensor<1x2x2x8xi32> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xi32>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xi32>) outs(%[[NEWF]] : tensor<2x2x6x8xi32>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xi32>, tensor<2x2x6x8xi32>) outs(%[[INIT]] : tensor<1x2x2x8xi32>) -> tensor<1x2x2x8xi32>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xi32>
+func.func @conv_2d_nhwc_fhwc_i32(%input: tensor<1x4x4x6xi32>, %filter: tensor<8x2x2x6xi32>, %init: tensor<1x2x2x8xi32>) -> tensor<1x2x2x8xi32> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xi32>, tensor<8x2x2x6xi32>)
+ outs (%init: tensor<1x2x2x8xi32>) -> tensor<1x2x2x8xi32>
+ return %0 : tensor<1x2x2x8xi32>
+}
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_i16
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xi16>, %[[FILTER:.+]]: tensor<8x2x2x6xi16>, %[[INIT:.+]]: tensor<1x2x2x8xi16>) -> tensor<1x2x2x8xi16> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xi16>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xi16>) outs(%[[NEWF]] : tensor<2x2x6x8xi16>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xi16>, tensor<2x2x6x8xi16>) outs(%[[INIT]] : tensor<1x2x2x8xi16>) -> tensor<1x2x2x8xi16>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xi16>
+func.func @conv_2d_nhwc_fhwc_i16(%input: tensor<1x4x4x6xi16>, %filter: tensor<8x2x2x6xi16>, %init: tensor<1x2x2x8xi16>) -> tensor<1x2x2x8xi16> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xi16>, tensor<8x2x2x6xi16>)
+ outs (%init: tensor<1x2x2x8xi16>) -> tensor<1x2x2x8xi16>
+ return %0 : tensor<1x2x2x8xi16>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_i8
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xi8>, %[[FILTER:.+]]: tensor<8x2x2x6xi8>, %[[INIT:.+]]: tensor<1x2x2x8xi8>) -> tensor<1x2x2x8xi8> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xi8>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xi8>) outs(%[[NEWF]] : tensor<2x2x6x8xi8>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xi8>, tensor<2x2x6x8xi8>) outs(%[[INIT]] : tensor<1x2x2x8xi8>) -> tensor<1x2x2x8xi8>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xi8>
+func.func @conv_2d_nhwc_fhwc_i8(%input: tensor<1x4x4x6xi8>, %filter: tensor<8x2x2x6xi8>, %init: tensor<1x2x2x8xi8>) -> tensor<1x2x2x8xi8> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xi8>, tensor<8x2x2x6xi8>)
+ outs (%init: tensor<1x2x2x8xi8>) -> tensor<1x2x2x8xi8>
+ return %0 : tensor<1x2x2x8xi8>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_q
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xf32>, %[[FILTER:.+]]: tensor<8x2x2x6xf32>, %[[INIT:.+]]: tensor<1x2x2x8xf32>, %[[A:.+]]: i32, %[[B:.+]]: i32) -> tensor<1x2x2x8xf32> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xf32>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xf32>) outs(%[[NEWF]] : tensor<2x2x6x8xf32>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf_q {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]], %[[A]], %[[B]] : tensor<1x4x4x6xf32>, tensor<2x2x6x8xf32>, i32, i32) outs(%[[INIT]] : tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xf32>
+ func.func @conv_2d_nhwc_fhwc_q(%input: tensor<1x4x4x6xf32>, %filter: tensor<8x2x2x6xf32>, %init: tensor<1x2x2x8xf32>, %a: i32, %b: i32) -> tensor<1x2x2x8xf32> {
+ %0 = linalg.conv_2d_nhwc_fhwc_q {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter, %a, %b: tensor<1x4x4x6xf32>, tensor<8x2x2x6xf32>, i32, i32)
+ outs (%init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
+ return %0 : tensor<1x2x2x8xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc_q"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_f32_unit_stride
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xf32>, %[[FILTER:.+]]: tensor<8x2x2x6xf32>, %[[INIT:.+]]: tensor<1x3x3x8xf32>) -> tensor<1x3x3x8xf32> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xf32>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xf32>) outs(%[[NEWF]] : tensor<2x2x6x8xf32>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xf32>, tensor<2x2x6x8xf32>) outs(%[[INIT]] : tensor<1x3x3x8xf32>) -> tensor<1x3x3x8xf32>
+// CHECK: return %[[CONV]] : tensor<1x3x3x8xf32>
+func.func @conv_2d_nhwc_fhwc_f32_unit_stride(%input: tensor<1x4x4x6xf32>, %filter: tensor<8x2x2x6xf32>, %init: tensor<1x3x3x8xf32>) -> tensor<1x3x3x8xf32> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xf32>, tensor<8x2x2x6xf32>)
+ outs (%init: tensor<1x3x3x8xf32>) -> tensor<1x3x3x8xf32>
+ return %0 : tensor<1x3x3x8xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: @conv_2d_nhwc_fhwc_f32_2_dialation
+// CHECK-SAME: (%[[INPUT:.+]]: tensor<1x4x4x6xf32>, %[[FILTER:.+]]: tensor<8x2x2x6xf32>, %[[INIT:.+]]: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32> {
+// CHECK-DAG: %[[NEWF:.+]] = tensor.empty() : tensor<2x2x6x8xf32>
+// CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[FILTER]] : tensor<8x2x2x6xf32>) outs(%[[NEWF]] : tensor<2x2x6x8xf32>) permutation = [1, 2, 3, 0]
+// CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%[[INPUT]], %[[TRANSPOSE]] : tensor<1x4x4x6xf32>, tensor<2x2x6x8xf32>) outs(%[[INIT]] : tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
+// CHECK: return %[[CONV]] : tensor<1x2x2x8xf32>
+func.func @conv_2d_nhwc_fhwc_f32_2_dialation(%input: tensor<1x4x4x6xf32>, %filter: tensor<8x2x2x6xf32>, %init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32> {
+ %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<2> : tensor<2xi64>,
+ strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter: tensor<1x4x4x6xf32>, tensor<8x2x2x6xf32>)
+ outs (%init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
+ return %0 : tensor<1x2x2x8xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.conv_2d_nhwc_fhwc"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.transpose_conv2d %0 : (!transform.any_op) -> (!transform.any_op)
+}
+
+// -----
----------------
nicolasvasilache wrote:
You could drop the split-input-file from tests and have only 1 common transform for all.
The transform would match both the "fhwc" version and the "fhwc_q" version.
This would also ensure that the op does not fail when passed empty handles.
https://github.com/llvm/llvm-project/pull/68567
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